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SimInf (version 6.2.0)

A Framework for Data-Driven Stochastic Disease Spread Simulations

Description

Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models.

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Install

install.packages('SimInf')

Monthly Downloads

490

Version

6.2.0

License

GPL-3

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Repository

https://github.com/stewid/SimInf

Maintainer

Stefan Widgren

Last Published

November 20th, 2018

Functions in SimInf (6.2.0)

Create a SISe model

Create an SEIR model

Set a template for where to write the V result matrix

Create a SISe3_sp model

Definition of the ‘SISe3_sp’ model

Definition of the ‘SEIR’ model

boxplot,SimInf_model-method

Box plot of number of individuals in each compartment

plot,SimInf_model-method

Display the outcome from a simulated trajectory

Model parser to define new models to run in SimInf

Extract the C code from a SimInf_model object

Run the SimInf stochastic simulation algorithm

Calculate prevalence from a model object with trajectory data

Example data with spatial distribution of nodes

Run SimInf_model on scaled parameters

Extract data from a simulated trajectory

Extract number of nodes in a model

A Framework for Data-Driven Stochastic Disease Spread Simulations

SimInf_events-class

Class "SimInf_events"

Definition of the ‘SISe3’ model

summary,SimInf_events-method

Detailed summary of a SimInf_events object

Example data to initialize the ‘SEIR’ model

Set a global data parameter for a SimInf_model object

summary,SimInf_model-method

Detailed summary of a SimInf_model object

Extract global data from a SimInf_model object

Create a ‘SISe3’ model

Create a '>SimInf_events object

SimInf_model-class

Class "SimInf_model"

Definition of the SIR model

Determine out-degree for each node in a model

Example data to initialize events for the ‘SIR’ model

Example data to initialize events for the ‘SEIR’ model

Example data to initialize the ‘SIR’ model

package_skeleton

Create a package skeleton from a SimInf_model

Example data to initialize the ‘SISe’ model

Create an SIR model

Create a SimInf_model

Determine in-degree for each node in a model

Example data to initialize events for the ‘SISe’ model

Extract local data from a node

Set a template for where to write the U result matrix

Example data to initialize events for the ‘SISe3’ model

Set the shift matrix for a SimInf_model object

show,SimInf_events-method

Brief summary of SimInf_events

show,SimInf_model-method

Brief summary of SimInf_model

Extract the shift matrix from a SimInf_model object

pairs,SimInf_model-method

Scatterplot of number of individuals in each compartment

Example data to initialize the ‘SISe3’ model

plot,SimInf_events-method

Display the distribution of scheduled events over time

select_matrix<-

Set the select matrix for a SimInf_model object

Extract the select matrix from a SimInf_model object

Definition of the SISe_sp model

Create a SISe_sp model

Definition of the SISe model

distance_matrix

Create a distance matrix between nodes for spatial models

Extract the events from a SimInf_model object